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Most previous studies on the scheduling problem in remanufacturing systems have focused on single or two production stages and economic criteria such as makespan or tardiness. However, environmental criteria like energy consumption or carbon emissions, as well as the lot-streaming production mode, have not been adequately addressed. This paper presents a novel approach to the energy-efficient remanufacturing system scheduling problem with lot-streaming production mode (ERSSP-LS) for the first time, which integrates disassembly, reprocessing, and reassembly three pivotal production stages. Firstly, a multi-objective mathematical model aimed at simultaneously minimizing the total energy consumption (TEC) and makespan (C max) is established and presented. To further reduce the TEC, the well-accepted energy-saving measure, known as the turn off and on strategy, is also integrated. Subsequently, a hybrid multi-objective optimization algorithm called HMOFFO, which combines the fruit fly optimization algorithm and simulated annealing mechanism is developed to seek the promising Pareto solution set. Finally, several experiments on a real-life case and a group of ten random test instances are carried out and the computational results affirm the feasibility and effectiveness of the proposed HMOFFO in solving the ERSSP-LS. [ABSTRACT FROM AUTHOR]

Recent years have seen a great deal of attention in energy conservation for production and manufacturing activities, particularly for energy-intensive industries. One of the useful strategies in reducing unnecessary energy consumption is to schedule these activities by considering both energy-driven and time-oriented criteria. This scheduling model can make an interaction between the energy consumption and the production cost to realize an efficient and sustainable production process. In this regard, the customers' expectation for due date is another important factor for decision-makers to control the delay in delivery. Making these decisions is extremely difficult due to uncertain circumstances to extract the accurate information of facilities and jobs in advance. Aforementioned issues in the context of urgent need for energy-conservation as well as the advent of globalized and multi-factory manufacture motivate our attempts to address a stochastic multi-objective distributed permutation flow shop scheduling problem by considering total tardiness constraint via minimizing the makespan and the total energy consumption. Due to the uncertainty of the proposed problem, a chance-constrain approach is used to describe decision-makers' awareness for the total tardiness, and accordingly, a chance-constrained programming model is utilized to formulate this problem. As a complicated optimization problem, a new multi-objective brain storm optimization algorithm incorporating stochastic simulation approach is specifically designed to better solve problem. A comparative study based on a set of benchmark test problems as well as two classical and popular algorithms is provided. The experimental results demonstrate that the proposed algorithm shows a very competitive performance in dealing with the investigated problem. [ABSTRACT FROM AUTHOR]

Abstract As the number of scrap cars increases rapidly, the management of their reverse logistics (RL) process has become an urgent issue for decision-makers in contemporary China. The process, capable of generating profits as well as minimizing negative environmental effects, mainly includes products acquisition, collection, inspection and disposition. Although the selection of take-back patterns is an essential part in the process, it is not well addressed by existing studies. Involving numerous related factors, the evaluation and selection of the take-back patterns of scrap cars are a comprehensive problem that can be formulated as a multi-criteria decision making (MCDM) problem. In this paper, we propose a complete method to solve it systematically. Three patterns quite suitable for China are presented and a novel criteria system is developed for choosing the best one. The unavoidable uncertainty in practical situation is also considered in this work. We integrate the grey method and Decision-making and Trial Evaluation Laboratory (grey DEMATEL) to determine the weights of nine criteria, and a fuzzy method and Vlse Kriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR) are adopted to rank the three patterns based on expert evaluations. Sensitivity analysis is also used to test and verify the robustness of the proposed method. Finally, we draw a conclusion that the Producer Responsibility Organization Take-back pattern whose main body is formed by manufacturer union organization is most suitable for China. The study results could provide a theoretical guideline for decision-makers in China as well as other developing countries. [ABSTRACT FROM AUTHOR]

Materials selection, as a essential link for manufacturing enterprises, has an important driving-force to comprehensively upgrade material properties and service life, especially in building and decoration fields. To qualitatively select the optimal green decoration materials, a hybrid multi-criteria decision making (MCDM) approach integrating analytical hierarchy process (AHP) and grey correlation technique for order performance by similarity to ideal solution (GC-TOPSIS) is proposed. The weights vector of hierarchical index structure, which is established based on interior environmental characteristics, i.e., physiological comfort, psychological satisfaction and interior environmental effect, is determined by AHP. GC-TOPSIS is applied to obtain the final ranking of green decoration materials to select the optimal one. A case study, i.e., 10 kinds of solid woods, is illustrated to validate the proposed method. Additionally, a sensitivity analysis of nine experiments is carried out to monitor the robustness of solution ranking to changes. The results proved that this method furnishes an rational and efficient decision support tool for performance assessment of green decoration materials. [ABSTRACT FROM AUTHOR]

While the concept of remanufacturing is gaining popularity in practice, its effect on environmental protection and energy saving is significant, which has attracted widespread concerns, especially in the automotive components field. Automotive components remanufacturing (ACR), as a prominent part in automotive life-cycle development, can dramatically reduce resources-waste and mitigate energy-crisis, which has become a prominent direction to promote sustainable development of Chinese economy. Thus, influence factors analysis and enterprise operation patterns assessment which primarily contribute to ACR industry development are of paramount importance. This paper elaborates the criteria influencing the ACR industry operation and analyzes the main operation patterns for ACR in China. To overcome the uncertainty of the evaluation process, a new hybrid multi-criterion decision-making (MCDM) model that combines fuzzy AHP (analytical hierarchy process) and fuzzy G-TOPSIS (combining gray relation analysis and technique for order performance by similarity to ideal solution) is proposed to determine weights of influence criteria and evaluate ACR production operation patterns. In addition, sensitivity analysis is applied to monitor the robustness of the method. The study refers to the experience of Chinese end-of-life vehicle (ELV) laws and implementation requirements of extended producer responsibility (EPR). The obtained results provide the new guideline to revise the regulations of Chinese ACR industry. [ABSTRACT FROM AUTHOR]

Disassembly sequencing has significant effects on the performance of remanufacturing and recycling of used or discarded products. Studies on disassembly sequence optimization have largely focused on sequential disassembly. However, for large or complex products sequential disassembly takes long time to complete and is rather inefficient since it removes only one part or subassembly at a time with only one operator assigned to disassemble a product. This work studies selective cooperative disassembly sequence planning (SCDSP) problem which is essential to disassemble large or complex products in an efficient way. Similar to sequential disassembly planning, SCDSP aims at finding the optimal disassembly task sequence, but is more complicated. SCDSP is a nonlinear NP-complete combinatorial optimization problem, and evolutionary algorithms can be adopted to solve it. In this paper exclusive and cooperative relationships are introduced as additional constraints besides the common precedence relationship. A novel procedure to generate feasible cooperative disassembly sequences (GFCDS) is proposed. A mathematical programming model of SCDSP is developed based on the parallel disassembly characteristics with two optimization objectives i.e. disassembly time and profit, considered. A multi-objective evolutionary algorithm (MOEA), i.e., multi-objective discrete artificial bee colony optimization (MODABC), is adopted to solve the problem to create the Pareto frontier. This approach is applied to real-world disassembly processes of two products (a small product and a medium/large one) to verify its feasibility and effectiveness. Also, the proposed method is compared with the well-known NSGA-II. For our comparative study, the nondominated solutions of the two MOEAs are compared in both cases, and two quantitative metrics, i.e., inverted generational distance ( IGD ) and spacing ( SP ), are adopted to further measure the algorithm performance. Results indicate that the set of nondominated solutions from MODABC are better for each instance tested, and the Pareto front is overall superior to that from NSGA-II. For both cases, IGD and SP are decreased by up to 81.5% and 62.2%, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Industrial process planning is to make an optimal decision in terms of resource allocation. The planning objective can be to minimize the time required to complete a task, maximize customer satisfaction by completing orders in a timely fashion and minimize the cost required to complete a task. Based on time and energy consumption in an industrial process planning problem, a novel energy analysis method is proposed to solve it. According to different constraints and credibility theory, typical expected value models of energy for it are presented. In addition, a hybrid intelligent optimization algorithm integrating fuzzy simulation, neural network and genetic algorithm is provided for solving the proposed expected value models. Some numerical examples are also given to illustrate the proposed concepts and the effectiveness of the used algorithm. [Copyright &y& Elsevier]

• Data mining of electrical energy consumption in home uses of Mashhad, Iran with extracting the consumption patterns. • Simulating implementation of a PhotoVoltaic (PV) system in the user's home. • Designing the motivation algorithm for Transformational Participation (TP) energy harvesting in a smart city. • Analyzing the sensitivity of future conditions by scenario wizard computing. Energy supply of megacities is considered as an active research topic in the new aspects of urban management, especially in developing countries like Iran. With an introduction to the sustainable development goals, the smart city concept presents a novel idea for providing energy in a city with the use of Artificial Intelligence (AI), renewable energy, such as Photovoltaic (PV) technologies, and Transformational Participation (TP) based on motivational programs for citizens. This study aims to evaluate the electrical energy consumption in Mashhad, Iran, based on machine learning tools and present the dynamic strategies for promoting citizens' willingness for renewable energy generation based on the experts' knowledge. The main novelty of this research is simultaneous application of Artificial Neural Network (ANN) and statistical analysis for creating a Decision Support System (DSS). Then, the solar energy potential is appraised by the PV system simulation tool during one year in our case study in Mashhad, Iran. Furthermore, a Classical Delphi (CD) method is applied for motivational strategies and further TP implementation. In particular, the motivational strategies are suggested by 45 experts and then are prioritized in sequential expert meetings. The outcomes of this research indicate that the ANN model can successfully forecast the electrical energy consumption in summer and winter periods with a 99% accuracy. Then, based on the solar energy computations in the PV system, the peak of electrical energy consumption can be controlled in the hottest and coldest months. Last but not least, the superposition of experts' and citizens' opinions reveal A4 (sharing benefits of optimized costs with the citizens by solar energy generation), B2 (reducing the electrical energy cost for solar energy generation, especially in peak times) and C1 (creating the energy coin in the city with credits instead of spending money in urban activities fits to solar energy generation) as the main motivational strategies for solar energy generation in short, middle and long-term planning horizons. [ABSTRACT FROM AUTHOR]

Design for disassembly (DFD) is an essential design technique to consider the disassembly and recyclability of a product at the initial design stage. An efficient DFD technique provides the disassembling and recycling easily. However, the recent trends of disassembling and recycling are to consider the aspects of environmental and social sustainability. Due to shortage of energy and deterioration of the ecological environment, the sustainable production is an active research topic to use the economic and social benefits as the evaluation standard of a design scheme as well as its environmental characteristics. This paper provides a new approach for DFD based on sustainability. In this regard, a hybrid multi-attribute decision making (MADM) method integrating the regret theory (RT) and the entropy weighting method is firstly developed. To implement the proposed approach, an eight-criterion evaluation system of schemes based on sustainability including the factors of disassembly energy consumption, disassembly accessibility, fastener ratio, toxic material proportion, material recovery rate, disassembly expense, production and use noise, and waste emissions, is established. To better describe the fuzziness of human thinking and to avoid information loss/distortion during information aggregation phases, the evaluation information given by experts is presented by our proposed interval 2-tuple linguistic intuitionistic fuzzy numbers (I2LIFNs). The weight vector of index structure is determined by the entropy weighting method under the fuzzy environment. The RT is employed to get the final order of alternatives by considering and quantitating both the risk attitude and the regret attitude of experts. To show the applicability of this study, a case study including four kinds of refrigerator schemes, is conducted to validate the proposed method. An extensive comparison with other recent and state of the art methods along with a sensitivity analysis of 13 experiments, is executed to verify effectiveness and reliability of the proposed I2LI-RT method. Finally, the experimental results show that: 1) disassembly accessibility (C 2), fastener ratio (C 3), waste emissions (C 8) and disassembly energy consumption (C 1) have a large impact on the scheme selection of DFD based on sustainability as those attributes carry relatively the larger weights; 2) our proposed method outperforms other recent and state of the art methods and 3) the chosen scheme A 1 is the winner in the majority of the sensitivity analysis cases (10 out of 13). At last but not least, the main finding is to furnish a systematic and efficient decision support tool for sustainable performance evaluation of product schemes of DFD. [ABSTRACT FROM AUTHOR]

Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance. [ABSTRACT FROM AUTHOR]

The concept of green manufacturing emphasizes the importance of product disassembly in achieving energy-efficient recycling and remanufacturing operations. Disassembly line balancing (DLB) is a critical component of the product disassembly process, wherein a set of tasks must be allocated to workstations for disassembly. This study proposes a multi-objective DLB model that aims to minimize multiple conflicting objectives simultaneously including idle rate, smoothness, labor cost, and energy consumption. A key innovation of this study involves the creation of a tailored adaptive large neighborhood search (ALNS) algorithm which is one of the first studies in the literature on product disassembly algorithms. The developed ALNS employs efficient construction and destruction heuristics to solve the proposed multi-objective DLB problem. The ALNS algorithm aims to destroy and repair solutions effectively, while a local search procedure helps it escape from local optimum solutions. The provided DLB model is effectively solved by applying the proposed ALNS algorithm to the disassembly process of a turbine reducer. The obtained results from this application serve as a compelling demonstration of the efficiency and effectiveness of the proposed approach. Furthermore, comparisons conducted with various state-of-the-art algorithms using small and large instance sets consistently highlight the superiority of the proposed ALNS approach. [ABSTRACT FROM AUTHOR]

We can formulate complex automation systems with advanced decision-making methods. This work proposes a new multi-criteria group-based decision-making (MCGDM) method based on the linguistic neutrosophic cloud (LNC). As an efficient linguistic expression, the linguistic neutrosophic set (LNS) introduces linguistic terminology into a neutrosophic set to make it more complex. However, there are inherent problems with linguistic values and neutrosophic sets. First, existing operators cannot handle linguistic neutrosophic numbers (LNN) with extreme values while producing distorted results. Second, the subscript-based computation of linguistic values does not reflect the change of ambiguity during the operation. Third, the literature review rarely considers the randomness of uncertain variables. To eliminate the drawbacks of previous studies, this paper proposes a multi-criteria group-based decision-making (MCGDM) method considering the linguistic neutrosophic cloud (LNC). The proposed method presents a distance measure for LNCs based on Wasserstein distance and develops an improved MCGDM method based on weighted modified partial Hausdorff distance. With an extensive simulation, the feasibility of the proposed method is verified by solving an auto part selection problem. Finally, we show the superiority of the proposed method through a comparison with four different aggregation operators of LNNs in the literature review. [ABSTRACT FROM AUTHOR]

The well-accepted three-stage remanufacturing system scheduling aims to achieve intelligent and green remanufacturing by reasonably coordinating limited resources in the system involving disassembly, reprocessing, reassembly production stages. Currently, the lot-streaming production mode is increasingly favoured by scholars and enterprise managers due to its remarkable performance in reducing machines' idle time and improving production efficiency. This paper investigates an energy-efficient scheduling issue for three-stage remanufacturing systems under the lot-streaming environment where each large-sized lot is split into its constituent small-sized sublots whose sizes may be inequal but remain consistent among various operations. Foremost, a dual-objective optimization mathematical model aiming at concurrently minimizing the makespan and total energy consumption is built. Then, since its NP-hard property, an improved fruit fly optimization (IFFO) algorithm is accordingly introduced. IFFO adopts a problem-specific three-layer encoding mechanism that contains three key pieces of scheduling information, i.e., lot sequence, machine assignment, and lot size splitting. Besides, based on the lot-streaming property, two distinct decoding strategies, i.e., sublot preemption and lot preemption are also correspondingly integrated. In addition, several effective optimization techniques, such as the simulated annealing-based replacement mechanism and Sigma method, are also employed to seek high-quality Pareto solutions. A real case and several designed random small/large-sized instances are tested on IFFO and its peers under three performance indicators. To obtain a convincing and solid conclusion, the Wilcoxon signed-rank statistical test is executed as well. The overall experimental results show that IFFO is feasible and effective in addressing the studied problem. [ABSTRACT FROM AUTHOR]

• Studied lean production and low-carbon management in remanufacturing systems. • Proposed decision-making method for lean low-carbon management. • Established functions to characterize carbon trail of remanufacturing production management. Remanufacturing is one of the effective ways to achieve carbon peak and carbon neutralization. Additionally, lean remanufacturing plays an irreplaceable role in promoting a circular economy and industrial green development. This paper focuses on the application of lean production and low-carbon management in remanufacturing assembly systems. We propose an optimization decision-making method for lean low-carbon management for remanufacturing assembly. On the basis of the carbon footprint, we interpret and measure the quality, cost, and time of remanufacturing assembly systems. Functions are established to characterize the carbon trajectory of production management. Then, a low-carbon optimization model of remanufacturing assembly production management is constructed to minimize carbon emissions with combined sorting of parts, and a solution method based on a genetic algorithm, which has the attributes of excellent quality, low cost, and high efficiency, is studied. Interestingly, through practical application in engine regeneration enterprises, we have found that integrating lean and low-carbon approaches can enhance the total factor productivity of the remanufacturing assembly system. This study provides practitioners and decision-makers with an effective way to formulate lean low-carbon management of remanufacturing. [ABSTRACT FROM AUTHOR]

With the increasing production and marketing of new energy vehicles (NEVs) in China, a large number of electric vehicles (EVs) batteries produced by the scrapped NEVs pose a great threat to environmental regulations and social security. Due to the influence of battery type, model, material, battery status, vehicle information and other factors, the scrapped new energy vehicle battery failed to achieve efficient and convenient recycling. Considering the requirements of some recently published government documents and the characteristics of electric vehicle battery, an integrated vehicle identification number (VIN) code is proposed. Based on the analysis of the current national standards GB 16735–2019 road vehicle-VIN identification number and GB/T 34,014–2017 code rules for vehicle power battery, the standard of combining battery code and tracking code is proposed. Finally, the possible coordination code is applied to a case study. The research results of this paper have been implanted into China's national standards. • Key factors affecting power battery recycling was put forward. • Finding coding issues is the core of all factors that affect recyclable traceability. • Car code, battery code, recycling code three-in-one was firstly proposed. • One code realizes the traceability of the entire life cycle of power batteries. [ABSTRACT FROM AUTHOR]

The recycling and dismantling of obsolete products is one of the frontier issues in the field of green remanufacturing. From the research status in this field, most scholars only use intelligent algorithms to optimize the disassembly sequence, and rarely evaluate the performance of obsolete products. On the basis of summarizing the existing research, this paper establishes a comprehensive disassembly evaluation model based on the fruit fly algorithm, crossover efficiency and extension-gray correlation degree. Firstly, a set of Pareto solutions is obtained by IFOA, and the group of 12 disassembly schemes is used as the decision-making unit (DMU) of the DEA. The average DQ of the 12 disassembly schemes was 5.4, the average EB was 387 CNY, and the average PDR was 83%. Then, the final cross-efficiency model is obtained by improving the traditional average cross-efficiency method. It is evaluated the performance of the comprehensive indicators of the disassembly plan in terms of time, economy and environment, and obtained their respective rankings. After the normalization process calculated, DMU 4 (0.0714) has a relatively higher ultimate weight than DMU 10 (0.0581). The analysis results show that the disassembly scheme evaluation result is the best overall performance of N 4. Finally, the same case is used to execute three optimization algorithms. The results show that IFOA has better optimization and sensitivity than the other two algorithms. [ABSTRACT FROM AUTHOR]

The issue of disassembly line balancing and remanufacturing has become an increasingly hot topic recently. However, little research focusing on carbon dioxide reduction and resource utilization has been done in the field of disassembly line balancing. For the purpose of being environmentally friendly, the disassembly line balancing should aim at low carbon emission, low energy consumption and cost reduction during the disassembly process. Considering this, this study proposes a multi-objective disassembly line balancing fruit fly optimization algorithm (MDFOA) for the disassembly sequence of obsolete agricultural machinery considering low-carbon design. We try to optimize the disassembly line balancing model using the fruit fly optimization algorithm. To satisfy the stability of MDFOA, the olfactory operation is cross-operated, and the Pareto non-dominated sorting method is adopted in the visual operation stage. In order to avoid falling into local optimum, a global cooperation mechanism is added to screen individuals with different degrees of dissimilarity. An illustrative example of the disassembly line of corn harvester cutting table is provided in this work. Comparison between our proposed MDFOA and other four frequently-used algorithms is conducted in this work. The results show that our method performs better than other algorithms for the disassembly line balancing problem (DLBP), thus verifying the practicability and feasibility of it. In the end, 12 Pareto solutions are obtained through MDFOA and we evaluated and selected the most satisfactory solution among them using Fuzzy AHP, for the determination of the optimal disassembly sequence. [ABSTRACT FROM AUTHOR]

• A new multi-objective optimization model is developed for ship schedule recovery. • Vessel speed reduction incentive programs and emission control areas are explicitly modeled. • Diverse schedule recovery options are considered within the designed modeling framework. • A customized algorithm is developed to solve the proposed multi-objective optimization model. • Detailed sensitivity analyses are performed to further examine various recovery options. Liner shipping networks often experience disruptions, and shipping lines must implement certain recovery measures. However, environmental concerns have to be considered as well. The implementation of vessel speed reduction incentive programs (VSRIPs) provides monetary incentives to shipping lines for sailing speed compliance in the vicinity of certain ports but, in the meantime, increases delays, since the speed-up option could not be used to the same extent. There is a scarcity of research that focuses on effectively addressing the competing objectives that arise during ship schedule recovery and incorporates emission reduction policies at ports. This study introduces a new multi-objective approach for ship schedule recovery, considering the presence of VSRIPs, emission control regulations, and a variety of recovery options. A customized algorithm inspired by the principles of dynamic secant approximation and the ε-constraint algorithm is developed and validated through extensive computational experiments. Detailed sensitivity analyses provide thorough managerial insights as well. [ABSTRACT FROM AUTHOR]

Abstract Reverse logistics is indispensable for resources reuse and circular economy, and a reverse logistics network optimization problem for end-of-life vehicles is studied frequently. Recent researches have focused on the material flow for different end-of-life vehicles. However, the primary question for an end-of-life vehicles recovery network is to determine optimal network nodes. To account for it, we considered a facility location allocation problem of end-of-life vehicles recovery network, and established a mathematical model to solve it. The model is used to achieve the minimization of cost for deciding optimal locations of end-of-life vehicles recovery network. The facility location allocation problem is a non-deterministic polynomial complete problem proved with increase in the number of candidate locations. This type of problem usually handled by a metaheuristics. Therefore, we proposed a valid novel approach based on artificial bee colony to solve the problem. Artificial bee colony is an optimization method that imitates bee behavior. Also, the proposed algorithm is applied to two different scale real-life cases, and some comparisons with several presented algorithms are presented to illustrate the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]

With the rapid technology advancement and market changes products are becoming outdated and subsequently discarded faster than ever before. As a result, recovery, recycling, and remanufacturing of end-of-life products are gaining more attention. Disassembly is indispensable to recycle and remanufacture end-of-life products, and a disassembly line is an efficient way to achieve it. A disassembly line balancing problem aims at optimizing the disassembly sequences in which the disassembly times of workstations are approximately equal and approaching the cycle time. However, in addition to line balancing assigning disassembly tasks to workstations needs to consider other factors, such as how to recover valuable components as fast as possible and reduce undesirable impacts on the environment as much as possible. This work aims at addressing a disassembly line balancing problem with multiple objectives, including economic and environmental factors and disassembly line efficiency. A three-phased methodology is proposed to solve this problem. The proposed method not only enables the consideration of objectives with interdependent weights in multi-criteria decision making but effectively generates a good enough trade-off disassembly solution using a 2-optimal algorithm. The proposed approach is tested on a benchmark and compared with three other methods. Experiment results are shown to verify its performance. [ABSTRACT FROM AUTHOR]

Low carbon manufacturing (LCM) is increasingly being regarded as the direction for technological innovation and implementation in industry. The manufacturing process chain plays a critical role in LCM to consider and define the whole production process, thus the analysis and evaluation method for its decision-making is urgently needed. Although interest in addressing carbon emission reduction in manufacturing is rising, the study of incorporating carbon emission reduction and economic improvement into the process chain is still considerably deficient. This work proposes a novel method to identify a process chain with the optimal carbon efficiency, where the carbon efficiency of process chain (CEpc) regarded as an effective indicator is evaluated by manufacturing value and carbon emissions defined based on eco-efficiency. In addition, the estimation models of manufacturing value and carbon emission are proposed, based on which, a decision-making model is established to solve the process chain decision-making issues with objective to maximize CEpc. An illustrative example of the sleeve part manufacturing process is conducted to demonstrate the effectiveness of this method. And the outcome can help decision makers to select the process chain with optimal carbon efficiency based on the dynamic change of production and different processing conditions. Furthermore, sensitivity analysis on material utilization rate, recycling rate as well as processing efficiency is presented to reveal the impacts of relevant improvement measures in CEpc for manufacturers. [ABSTRACT FROM AUTHOR]

Fuzzy Petri nets (FPNs) are a potential modeling technique for knowledge representation and reasoning of rule-based expert systems. To date, many studies have focused on the improvement of FPNs and various new algorithms and models have been proposed in the literature to enhance the modeling power and applicability of FPNs. However, no systematic and comprehensive review has been provided for FPNs as knowledge representation formalisms. Giving this evolving research area, this work presents an overview of the improved FPN theories and models from the perspectives of reasoning algorithms, knowledge representations and FPN models. In addition, we provide a survey of the applications of FPNs for solving practical problems in variety of fields. Finally, research trends in the current literature and potential directions for future investigations are pointed out, providing insights and robust roadmap for further studies in this field. [ABSTRACT FROM AUTHOR]

Remanufacturing is a practice of growing importance as it returns the end of life products back to conditions that is as good as or better than new ones. The increasingly stringent environmental legislation and economic demands has led to the rapid development of remanufacturing industry in the world. Remanufacturing is still at its infantry. One of the major challenges faced by remanufacturing is to guarantee the reliability of remanufactured products since they came from cores with varying condition. Process planning plays a critical role in realizing a successful remanufacturing strategy since it directly affects the success rate of remanufacturing as well as reliability and cost. To do so, this work presents an optimization method for remanufacturing process planning in which reliability and cost are taken into consideration. In this method, reliability is represented by failure rate of remanufacturing operations which is influenced by the quality of returned used products (cores), whilst process cost includes machine cost and tool cost. The multi-objective optimization problem is solved by a genetic algorithm. To assess the usefulness and practicality of the proposed method, an illustrative example is given to illustrate the proposed models and the effectiveness of the proposed algorithm. The results showed that the proposed method is effective for improving reliability and reducing cost. [ABSTRACT FROM AUTHOR]

Nowadays, Machine Learning (ML) techniques have become one of the most widely used engineering tools due to their numerous advantages, including their continuous improvement. This study proposes a smart soft sensor using various ML algorithms to control and predict the Coagulation and Flocculation Process (CFP). Optimizing and predicting the behaviour of a CFP is difficult due to its non-linear and complex behaviour. Therefore, ML computations is a proper method to overcome this challenge. However, one of the challenges of ML studies is the lack of sufficient data which we overcome by using an 8-year database of experiments. For prediction, this study compares different ML methods, including Random Tree, Random Forest (RF), Artificial Neural Networks (ANN), Quinlan's M5 algorithm with regression function (M5P), Linear Regression (LR), Simple LR, Gaussian method, Decision Stump method, Smola and Scholkopf's Sequential Minimal Optimization algorithm with LR (SMOreg), and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Also, for optimization of the studied system, Central Composite Design designed the experimental data with the Response Surface Methodology (CCD-RSM). The most significant factors in turbidity removal are related to FeCl3 dosage, and slow mixing speed with < 0.0001 and 0.005 P-values. The present research findings show that the maximum removal efficiency of 92% is predicted using CCD-RSM under the optimal condition. In addition, ANFIS and RF models with R2 of 0.96 and 0.92, have shown the highest accuracy levels for removing water turbidity. Finally, a Petri-Net model establishes a Conceptual model to intelligently conduct managerial insights for water treatments. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents an experimental study on the effects of using agricultural waste coconut shells for producing synthesized biomass recycled aggregate (SBRA) and replacing natural aggregate (NA) with its equivalent amount on the durability, thermal conductivity, carbon dioxide emissions, and economics of concrete. Test results show that the 3-day and 28-day compressive strength of concrete with the addition of SBRA - Class III reached 93% and 88% of natural aggregate concrete (NAC), respectively. In addition, using SBRA can effectively reduce the thermal conductivity of concrete, which shows great potential for building energy conservation. Compared with NA, the use of SBRA can reduce CO 2 emissions and the cost of concrete by 2% and 5.9–7%, respectively. And the compressive strength - CO 2 emission efficiency and compressive strength - cost efficiency are comparable to NA. [ABSTRACT FROM AUTHOR]

Eco-design that addresses the consumption of resources and energy, as well as pollution emissions from a product life-cycle perspective, is an effective way to solve environmental problems. However, the optimal eco-design solution is hardly generated in the conceptual design stage due to the complex association of the life cycle design information. To this end, this paper proposes a new eco-design model based on a life cycle integrated framework to effectively manage life cycle information in the conceptual design phase. In this work, (1) The life cycle-oriented model is proposed to integrate the product design information in terms of a systematic association mechanism between function, structure, material and process to construct the design space. (2) A life cycle design scenario-based similarity matching method is developed to support the environmental impact assessment of design options in the design space. (3) A hope tree-based heuristic search algorithm with the principle of minimizing environmental impact is proposed to efficiently obtain the eco-design solution. An application for eco-design of the lift equipment is given to demonstrate the capability of the proposed method. The results show that the environmental impact of the generated eco-design solution is improved by 19.48%, and the material and use stage has the highest environmental impact which accounts for 39.59% and 47.74% respectively. This work provides a targeted method for guiding designers to implement eco-design in the conceptual design phase through the life cycle. [Display omitted] • An integrated life cycle model is proposed to express the design information of function, structure, material and process. • A scenario-based similarity matching method is proposed to acquire the LCI for the design options. • Hope tree-based algorithm is used to efficiently obtain the optimal eco-design solution. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a positioning method to extract the feature points employing the singular value decomposition for a target board image as an example. In order to discriminate the feature points from the noises existing in the image, the image matrix is factorized into three matrices. The geometrical implication of the three matrices is interpreted by three transformations which are rotation, scaling, and another rotation. The singular values in the diagonal of the scaling matrix are arranged in descending order, which stands for the significance sequence of the image features. The smaller singular values are corresponding to the noises while the greater ones are considered as primary features. Therefore the new singular values matrix is defined by the modified original scaling matrix in which the smaller singular values are removed by a cutting point. The smoothing image is reconstructed by the two original rotation matrices and the new singular values matrix with the same arrangement. The latter detection procedure of the feature points adopts the Harris corner detection method to evaluate the singular value decomposition filter. The experiments are performed on the target images with the noise densities of 0.005, 0.01 and 0.02 respectively. Comparing with the traditional approach, the feature points on the target which are considered as the primary features are preserved by the filter. Moreover, the noises vanish in the images because they are unimportant details. The experiments prove that the outlined method has the potential to feature points positioning as well as appropriateness for the manufacture engineering and mechanical inspection. [Copyright &y& Elsevier]

• A vehicle routing problem with a factory-in-a-box in multi-objective settings is studied. • A multi-objective formulation is proposed to compromise the conflicting cost components. • A novel customized nature-inspired evolutionary algorithm is developed to solve the problem. • Numerical experiments are performed considering well-known exact and metaheuristic methods. • A detailed analysis of solutions is conducted to draw managerial insights. Emergencies, such as pandemics (e.g., COVID-19), warrant urgent production and distribution of goods under disrupted supply chain conditions. An innovative logistics solution to meet the urgent demand during emergencies could be the factory-in-a-box manufacturing concept. The factory-in-a-box manufacturing concept deploys vehicles to transport containers that are used to install production modules (i.e., factories). The vehicles travel to customer locations and perform on-site production. Factory-in-a-box supply chain optimization is associated with a wide array of decisions. This study focuses on selection of vehicles for factory-in-a-box manufacturing and decisions regarding the optimal routes within the supply chain consisting of a depot, suppliers, manufacturers, and customers. Moreover, in order to contrast the options of factory-in-a-box manufacturing with those of conventional manufacturing, the location of the final production is determined for each customer (i.e., factory-in-a-box manufacturing with production at the customer location or conventional manufacturing with production at the manufacturer locations). A novel multi-objective optimization model is presented for the vehicle routing problem with a factory-in-a-box that aims to minimize the total cost associated with traversing the edges of the network and the total cost associated with visiting the nodes of the network. A customized multi-objective hybrid metaheuristic solution algorithm that directly considers problem-specific properties is designed as a solution approach. A case study is performed for a vaccination project involving factory-in-a-box manufacturing along with conventional manufacturing. The case study reveals that the developed solution method outperforms the ε-constraint method, which is a classical exact optimization method for multi-objective optimization problems, and several well-known metaheuristics. [ABSTRACT FROM AUTHOR]

For purpose of improving the waste heat recovery and utilization efficiency of high-temperature semi-cokes, a new heat exchanger with primary recovery method was creatively proposed, and the heat transfer performance of flowing irregular semi-cokes in the new primary heat exchanger was investigated experimentally for the first time. The effects of initial temperature, flow velocity and diameter of semi-cokes on the heat transfer performance of flowing irregular semi-cokes were analyzed, and the experimental correlation formula of Nusselt number between flowing irregular semi-coke and cooling wall was studied by dimensional analysis and experimental data fitting. Furthermore, the heat transfer performance of flowing irregular semi-cokes under the condition of steam flow was compared with those under the condition of no steam flow. Experiment results show that the initial temperature, flow velocity and diameter of semi-coke have significant effects on the heat transfer performance, and the initial temperature and flow velocity have positive effects. The experimental correlation formula of Nusselt number was obtained, and the average relative error of calculation and experiment is 4.46%, which indicates that the experimental correlation formula can well predict heat transfer performance. The heat transfer performance of flowing irregular semi-cokes under the condition of steam flow is better than that under the condition of no steam flow, and the heat transfer mechanism under the condition of steam flow was revealed. Meanwhile, the comprehensive heat transfer coefficient with steam flow is 79.5% higher than that without steam flow. The steam acts as an intermediate heat transfer medium of the flowing irregular semi-cokes and cooling wall, which occupies an important position on the heat transfer enhancement between the flowing irregular semi-cokes and cooling wall. [ABSTRACT FROM AUTHOR]

• An integrated optimization model is developed for liner shipping. • The model captures all the major tactical-level planning decisions. • The deployment of a heterogeneous ship fleet is allowed at each route. • A decomposition-based heuristic is designed to solve the model. • Numerical experiments demonstrate the efficiency of the heuristic. • Managerial insights are illustrated using the developed model. The maritime transportation flows and container demand have been increasing over time, although the COVID-19 pandemic may slow down this trend for some time. One of the common strategies adopted by shipping lines to efficiently serve the existing customers is the deployment of large ships. The current practice in the liner shipping industry is to deploy a combination of ships of different types with different carrying capacities (i.e., heterogeneous fleet), especially at the routes with a significant demand. However, heterogeneous fleets of ships have been investigated by a very few studies addressing the tactical liner shipping decisions (i.e., determination of service frequency, ship fleet deployment, optimization of ship sailing speed, and design of ship schedules). Moreover, limited research efforts have been carried out to simultaneously capture all the major tactical liner shipping decisions using a single solution methodology. Therefore, this study proposes an integrated optimization model that addresses all the major tactical liner shipping decisions and allows the deployment of a heterogeneous ship fleet at each route, considering emissions generated throughout liner shipping operations. The model's objective maximizes the total turnaround profit generated from liner shipping operations. A decomposition-based heuristic algorithm is presented in this study to solve the model proposed and efficiently tackle large-size problem instances. Numerical experiments, carried out for a number of real-world liner shipping routes, demonstrate the effectiveness of the proposed methodology. A set of managerial insights, obtained from the proposed methodology, are also provided. [ABSTRACT FROM AUTHOR]

Proper estimation of electricity consumption is one of the influential factors for sustainability and cleaner production in both developed and developing countries. Many studies have been conducted to present accurate prediction models for forecasting electricity demand. However, researchers are still working to develop models with higher accuracy. This study applies a newer branch of Genetic Programming (GP) as a soft computing technique, known as Multi Expression Programming (MEP) to predict the electricity consumption of China for the first time based on the data collected from 1991 to 2019. Specifically, a robust mathematical model was developed using MEP for this purpose. Different predictive techniques known as Gene Expression Programming (GEP) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used to compare the accuracy of the model. Based on the results, the proposed MEP model is more powerful and accurate than both GEP and ANFIS. In addition, a sensitivity analysis was conducted to present the impact of each factor on the electricity consumption of China. It was shown that among the four independent factors (Population, Gross Domestic Product (GDP), Import, and Export), Population has the highest impact, followed by Export, Import and GDP, respectively. [ABSTRACT FROM AUTHOR]

In order to realize the waste heat recovery of the high-temperature semi-cokes, the heat exchanger with one time heat transfer was put forward and the fractal heat conduction model was established for the first time. The internal structure of semi-cokes with different diameters in the heat exchanger was analyzed, and the internal structure was taken into account in the fractal heat conduction model. Furthermore, the fractal heat conduction model is demonstrated through the experiment, and it is compared with the traditional volume average model. The results showed that, the internal structure of semi-cokes in the heat exchanger determines the fractal heat conduction characteristics, and the influence of porosity on the equivalent thermal conductivity calculated by the fractal heat conduction model is less than the fractal dimension and the contact resistance number. The equivalent thermal conductivity calculated by the fractal heat conduction model is closer to experimental values than that calculated by the traditional volume average model, and the error of the equivalent thermal conductivity between the experimental values and the fractal heat conduction model is less than 6%, thus the fractal heat conduction model is valid and feasible. • A new waste heat recovery system of semi-coke was put forward. • The internal structure of semi-coke bed was obtained and analyzed. • The fractal heat conduction model was established for the first time. • The fractal heat conduction model is superior to traditional volume average model. [ABSTRACT FROM AUTHOR]